284 citations · 806 across the 87 of their papers we have counts for
13 papers · 1 filter
A Generalized Heckman Model With Varying Sample Selection Bias and Dispersion Parameters
Fernando de S. Bastos, Wagner Barreto-Souza, Marc G. Genton
Many proposals have emerged as alternatives to the Heckman selection model, mainly to address the non-robustness of its normal assumption. The 2001 Medical Expenditure Panel Survey…
Assessing the Reliability of Wind Power Operations under a Changing Climate with a Non-Gaussian Bias Correction
Jiachen Zhang, Paola Crippa, Marc G. Genton +1
Facing increasing societal and economic pressure, many countries have established strategies to develop renewable energy portfolios, whose penetration in the market can alleviate t…
Scalable computation of predictive probabilities in probit models with Gaussian process priors
Jian Cao, Daniele Durante, Marc G. Genton
Predictive models for binary data are fundamental in various fields, and the growing complexity of modern applications has motivated several flexible specifications for modeling th…
Are You All Normal? It Depends!
Wanfang Chen, Marc G. Genton
The assumption of normality has underlain much of the development of statistics, including spatial statistics, and many tests have been proposed. In this work, we focus on the mult…
High Performance Multivariate Geospatial Statistics on Manycore Systems
Mary Lai O. Salvaña, Sameh Abdulah, Huang Huang +4
Modeling and inferring spatial relationships and predicting missing values of environmental data are some of the main tasks of geospatial statisticians. These routine tasks are acc…
Test and Visualization of Covariance Properties for Multivariate Spatio-Temporal Random Fields
Huang Huang, Ying Sun, Marc G. Genton
The prevalence of multivariate space-time data collected from monitoring networks and satellites, or generated from numerical models, has brought much attention to multivariate spa…
Conditional Normal Extreme-Value Copulas
Pavel Krupskii, Marc G. Genton
We propose a new class of extreme-value copulas which are extreme-value limits of conditional normal models. Conditional normal models are generalizations of conditional independen…
A Pairwise Hotelling Method for Testing High-Dimensional Mean Vectors
Zongliang Hu, Tiejun Tong, Marc G. Genton
For high-dimensional small sample size data, Hotelling's T2 test is not applicable for testing mean vectors due to the singularity problem in the sample covariance matrix. To overc…
Exploiting Low Rank Covariance Structures for Computing High-Dimensional Normal and Student- Probabilities
Jian Cao, Marc G. Genton, David E. Keyes +1
We present a preconditioned Monte Carlo method for computing high-dimensional multivariate normal and Student- probabilities arising in spatial statistics. The approach combines…
Geostatistical Modeling and Prediction Using Mixed-Precision Tile Cholesky Factorization
Sameh Abdulah, Hatem Ltaief, Ying Sun +2
Geostatistics represents one of the most challenging classes of scientific applications due to the desire to incorporate an ever increasing number of geospatial locations to accura…
Nonparametric Trend Estimation in Functional Time Series with Application to Annual Mortality Rates
Israel Martínez-Hernández, Marc G. Genton
Here, we address the problem of trend estimation for functional time series. Existing contributions either deal with detecting a functional trend or assuming a simple model. They c…
Vector Autoregressive Models with Spatially Structured Coefficients for Time Series on a Spatial Grid
Yuan Yan, Hsin-Cheng Huang, Marc G. Genton
We propose a parsimonious spatiotemporal model for time series data on a spatial grid. Our model is capable of dealing with high-dimensional time series data that may be collected…
Recent Developments in Complex and Spatially Correlated Functional Data
Israel Martínez-Hernández, Marc G. Genton
As high-dimensional and high-frequency data are being collected on a large scale, the development of new statistical models is being pushed forward. Functional data analysis provid…